Method for operating a process plant
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Solution Overview
Problem
Existing process plants face challenges in efficient operation due to limited direct measurement of process states, nonlinear dynamic responses, and the need for extensive tuning of control strategies, which can lead to suboptimal performance and increased energy consumption.
Innovation Solution
A dynamic model based on thermo-fluidic and thermo-dynamic correlations is used to estimate unmeasured process parameters and predict plant behavior, enabling improved control strategies and pre-tuning of controllers, reducing the reliance on single-point measurements and allowing for faster, more aggressive control adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dynamic model based on thermo-fluidic and thermo-dynamic correlations is used to estimate unmeasured process parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
A dynamic model acts as an intermediary between available measurements and unmeasured process parameters. The model uses thermo-fluidic and thermo-dynamic correlations to compute estimated values of unmeasured parameters based on measured inputs, effectively mediating the information gap without requiring direct physical sensors for all parameters.
Solution Approach 2:
The dynamic model creates a virtual copy or representation of the process plant's behavior. This computational model replicates the physical system's dynamics using mathematical correlations, allowing estimation of parameters by observing the model's state rather than directly measuring all physical quantities.
2Productivity
If the dynamic model is used for pre-tuning of controllers and developing control strategies offline, then productivity is improved, but device complexity increases
Solution Approach 1:
Controller tuning and control strategy development are performed in advance during the offline phase using the dynamic model. This preliminary action allows controllers to be pre-configured and optimized before actual plant commissioning, reducing on-site commissioning time and accelerating productivity without requiring complex real-time adjustments during startup.
3Speed
If the dynamic model enables faster and more aggressive control adjustments, then speed of response is improved, but reliability may worsen due to nonlinear dynamic responses
Solution Approach 1:
The control system leverages the dynamic model's capability to predict nonlinear dynamic responses, enabling faster and more aggressive control adjustments. By understanding the system's dynamic behavior through the model, controllers can be tuned to respond more quickly while maintaining stability, transforming the challenge of nonlinear dynamics into an opportunity for improved performance.
Solution Approach 2:
The dynamic model provides a framework for implementing advanced feedback control strategies. By continuously comparing model predictions with actual measurements and adjusting control actions accordingly, the system achieves faster response while maintaining reliability through model-based feedback mechanisms that account for nonlinear dynamics.
Data Source
AI summary
A method for operating a process plant using a dynamic model of the process plant, the dynamic model being based on at least one of thermo fluidic correlations, thermo dynamic correlations, phenomenological correlations, and equations, and being based on geometry and/or topology of components of the process plant, the dynamic model receiving process parameters as input values, the dynamic model being adapted to represent a transition from one to another state of the process plant, wherein the dynamic model is used in an online mode, in which the dynamic model is used in parallel with the operation of the process plant, wherein signals from a control system of the process plant, the signals representing values of at least one first process parameter, are received and fed into the dynamic model.